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However, prevailing approaches often suffer from fragmented perception, shallow reasoning, and delayed scheduling, leading to a lack of coordination among critical system modules. This deficiency significantly hinders holistic intelligent decision-making and real-time regulation under rapidly changing conditions. To address these challenges, this paper proposes a\u00a0collaborative framework that integrates digital twins with metaverse-based semantic modeling. A three-layer architecture is constructed, consisting of the\u00a0physical infrastructure layer,\u00a0virtual twin resource layer, and\u00a0traffic situation awareness layer, thereby forming a closed-loop mechanism of\u00a0perception\u2013reasoning\u2013scheduling. The proposed system leverages the immersive semantic environment provided by the metaverse to enable contextual interpretation and intent recognition of traffic data. This semantic understanding drives the dynamic evolution and causal reasoning of digital twin entities. Based on the inferred results, a\u00a0cloud\u2013edge\u2013end collaborative scheduling strategy\u00a0is triggered to allocate resources adaptively. In addition, an interactive feedback mechanism is incorporated to support the real-time verification and continuous optimization of scheduling outcomes. Within this framework, we design a\u00a0metaverse-driven Mixture-of-Experts perception network\u00a0that enables multi-level semantic recognition and prediction of global traffic trends, regional congestion, and local anomalies. Furthermore, we introduce a\u00a0multi-agent scheduling mechanism\u00a0that combines\u00a0virtualized resource mapping\u00a0with\u00a0structure-aware transfer strategies, thereby enhancing the generalization capacity and dynamic responsiveness of scheduling policies across heterogeneous infrastructure environments. Extensive experimental evaluations demonstrate that the proposed approach outperforms existing mainstream methods across several key metrics, including task acceptance rate, long-term revenue, congestion mitigation effectiveness, and resource utilization efficiency. These results validate the proposed framework\u2019s superior adaptability and system-level of intelligence in complex traffic scenarios.<\/jats:p>","DOI":"10.1007\/s10462-025-11455-9","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T14:45:42Z","timestamp":1765205142000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A collaborative metaverse-digital twin system for traffic perception, reasoning, and resource scheduling"],"prefix":"10.1007","volume":"59","author":[{"given":"Zhongnan","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"issue":"1","key":"11455_CR1","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1109\/TIV.2023.3332739","volume":"9","author":"Y Ai","year":"2023","unstructured":"Ai Y, Liu Y, Gao Y et al (2023) PMWorld: a parallel testing platform for autonomous driving in mines. 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